Nonlinear model predictive position control for a tail-actuated robotic fish

Nonlinear Dynamics - Position control is a significant technique for the underwater application of robotic fish; however, it is also very challenging due to the underactuated property and input...     

Nonlinear model predictive control based on Nelder Mead optimization method

In this paper, a model predictive control (MPC) scheme based on Hammerstein model is carried on. The use of such nonlinear models complicates the implementation of the MPC in terms of computational time and burden since a nonlinear and so a nonconvex optimization problem will result. The Nelder Mead (NM) algorithm, as a free derivative method, is used to solve the resulting optimization problem. NM algorithm proves its efficiency in terms of computation time and global optimum seeking that can be successfully exploited especially with fast dynamic systems. A comparative study between the NM algorithm and the gradient-based method (GBM) based on computation time is established. The efficiency of the NM algorithm is illustrated with SISO and MIMO examples compared to GBM algorithm.     

Nonlinear model predictive control based on piecewise linear Hammerstein models

This paper develops a nonlinear model predictive control (MPC) algorithm for dynamic systems represented by piecewise linear (PWL) Hammerstein models. At each sampling instant, the predicted output trajectory is linearized online at an assumed input trajectory such that the control actions can be easily calculated by solving a quadratic programming optimization problem, and such linearization and optimization may be repeated a few times for good linear approximation accuracy. A three-step procedure is developed to linearize a PWL function, where the derivatives of a PWL function are obtained by a computationally efficient look-up table approach. Unlike many existing MPC algorithms for Hammerstein systems, it does not require the inversion of static nonlinearity and can directly cope with input constraints even in multivariable systems. Two benchmark chemical reactors are studied to illustrate the effectiveness of the proposed algorithm.     

带有末端角度和轨迹路径点约束的MPSC制导律设计

针对多约束制导问题,给出了一种同时满足末端角度约束和飞行轨迹路径点约束的模型预测扩展控制制导方法,该制导方法通过满足飞行轨迹路径点约束实现灵活调节飞行轨迹,可以大大缩短目标防御反应时间。模型预测扩展控制制导方法是基于非线性最优控制理论,给出了控制量表达式以二次形式近似时制导律的设计过程。模型预测扩展控制制导方法只能对末端时刻输出量进行约束,通过对该制导方法进行扩展,使其还可以满足飞行轨迹路径点约束。仿真结果表明,考虑飞行轨迹路径点约束时,导弹经过设定的路径点并以给定的弹道倾角命中目标。     

Stability and optimal control strategies for a novel epidemic model of COVID-19

Nonlinear Dynamics - In this paper, a novel two-stage epidemic model with a dynamic control strategy is proposed to describe the spread of Corona Virus Disease 2019 (COVID-19) in China. Combined...     

Nonlinear dynamic analysis of an epidemiological model for COVID-19 including public behavior and government action

Nonlinear Dynamics - This paper is concerned with nonlinear modeling and analysis of the COVID-19 pandemic currently ravaging the planet. There are two objectives: to arrive at an appropriate model...     

Nonlinear dynamics of COVID-19 pandemic: modeling,control, and future perspectives

Nonlinear Dynamics -     

Synchronized nonpharmaceutical interventions for the control of COVID-19

Nonlinear Dynamics - The world is experiencing an ongoing pandemic of coronavirus disease-2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In...     

Vaccination control of an epidemic model with time delay and its application to COVID-19

Nonlinear Dynamics - This paper studies an SEIR-type epidemic model with time delay and vaccination control. The vaccination control is applied when the basic reproduction number $$R_0>1$$ ....     

A stochastic SEIHR model for COVID-19 data fluctuations

Nonlinear Dynamics - Although deterministic compartmental models are useful for predicting the general trend of a disease’s spread, they are unable to describe the random daily fluctuations...     

Dynamics and optimal control of a stochastic coronavirus (COVID-19) epidemic model with diffusion

In view of the facts in the infection and propagation of COVID-19, a stochastic reaction–diffusion epidemic model is presented to analyse and control this infectious diseases. Stationary distribution and Turing instability of this model are discussed for deriving the sufficient criteria for the persistence and extinction of disease. Furthermore, the amplitude equations are derived by using Taylor series expansion and weakly nonlinear analysis, and selection of Turing patterns for this model can be determined. In addition, the optimal quarantine control problem for reducing control cost is studied, and the differences between the two models are compared. By applying the optimal control theory, the existence and uniqueness of the optimal control and the optimal solution are obtained. Finally, these results are verified and illustrated by numerical simulation.

     

Dynamics and control of COVID-19 pandemic with nonlinear incidence rates

World Health Organization (WHO) has declared COVID-19 a pandemic on March 11, 2020. As of May 23, 2020, according to WHO, there are 213 countries, areas or territories with COVID-19 positive cases. To effectively address this situation, it is imperative to have a clear understanding of the COVID-19 transmission dynamics and to concoct efficient control measures to mitigate/contain the spread. In this work, the COVID-19 dynamics is modelled using susceptible–exposed–infectious–removed model with a nonlinear incidence rate. In order to control the transmission, the coefficient of nonlinear incidence function is adopted as the Governmental control input. To adequately understand the COVID-19 dynamics, bifurcation analysis is performed and the effect of varying reproduction number on the COVID-19 transmission is studied. The inadequacy of an open-loop approach in controlling the disease spread is validated via numerical simulations and a robust closed-loop control methodology using sliding mode control is also presented. The proposed SMC strategy could bring the basic reproduction number closer to 1 from an initial value of 2.5, thus limiting the exposed and infected individuals to a controllable threshold value. The model and the proposed control strategy are then compared with real-time data in order to verify its efficacy.

     

A spread model of COVID-19 with some strict anti-epidemic measures

In the absence of specific drugs and vaccines, the best way to control the spread of COVID-19 is to adopt and diligently implement effective and strict anti-epidemic measures. In this paper, a mathematical spread model is proposed based on strict epidemic prevention measures and the known spreading characteristics of COVID-19. The equilibria (disease-free equilibrium and endemic equilibrium) and the basic regenerative number of the model are analyzed. In particular, we prove the asymptotic stability of the equilibria, including locally and globally asymptotic stability. In order to validate the effectiveness of this model, it is used to simulate the spread of COVID-19 in Hubei Province of China for a period of time. The model parameters are estimated by the real data related to COVID-19 in Hubei. To further verify the model effectiveness, it is employed to simulate the spread of COVID-19 in Hunan Province of China. The mean relative error serves to measure the effect of fitting and simulations. Simulation results show that the model can accurately describe the spread dynamics of COVID-19. Sensitivity analysis of the parameters is also done to provide the basis for formulating prevention and control measures. According to the sensitivity analysis and corresponding simulations, it is found that the most effective non-pharmaceutical intervention measures for controlling COVID-19 are to reduce the contact rate of the population and increase the quarantine rate of infected individuals.

     

带多个弹道路径点约束的扩展MPSP制导方法

针对弹道导弹临近空间低弹道突防问题,对仅仅满足末端输出量约束的模型预测静态规划(MPSP)制导方法进行扩展,提出了带弹道路径点约束的扩展MPSP制导方法。通过在临近空间中段飞行弹道上设置一系列弹道路径点约束,依靠这种策略调节中段飞行弹道的形状,提高弹道导弹的机动突防能力。通过仿真对扩展MPSP制导方法的有效性进行验证,仿真结果表明扩展MPSP制导方法能很好地满足临近空间中段低弹道飞行弹道路径点约束,弹道路径点处的角度偏差可控制在0.1°范围内,位移偏差可控制在1.0 m范围内。     

Geospatial model of COVID-19 spreading and vaccination with event Gillespie algorithm

We have developed a mathematical model and stochastic numerical simulation for the transmission of COVID-19 and other similar infectious diseases that accounts for the geographic distribution of population density, detailed down to the level of location of individuals, and age-structured contact rates. Our analytical framework includes a surrogate model optimization process to rapidly fit the parameters of the model to the observed epidemic curves for cases, hospitalizations, and deaths. This toolkit (the model, the simulation code, and the optimizer) is a useful tool for policy makers and epidemic response teams, who can use it to forecast epidemic development scenarios in local settings (at the scale of cities to large countries) and design optimal response strategies. The simulation code also enables spatial visualization, where detailed views of epidemic scenarios are displayed directly on maps of population density. The model and simulation also include the vaccination process, which can be tailored to different levels of efficiency and efficacy of different vaccines. We used the developed framework to generate predictions for the spread of COVID-19 in the canton of Geneva, Switzerland, and validated them by comparing the calculated number of cases and recoveries with data from local seroprevalence studies.

     

Learning-based nonlinear model predictive control with accurate uncertainty compensation

Nonlinear Dynamics - A learning-based nonlinear model predictive control (LBNMPC) method is proposed in this paper for general nonlinear systems under system uncertainties and subject to state and...     

Distributed stochastic model predictive control for energy dispatch with distributionally robust optimization

A chance-constrained energy dispatch model based on the distributed stochastic model predictive control (DSMPC) approach for an islanded multi-microgrid system is proposed. An ambiguity set considering the inherent uncertainties of renewable energy sources (RESs) is constructed without requiring the full distribution knowledge of the uncertainties. The power balance chance constraint is reformulated within the framework of the distributionally robust optimization (DRO) approach. With the exchange of information and energy flow, each microgrid can achieve its local supply-demand balance.Furthermore, the closed-loop stability and recursive feasibility of the proposed algorithm are proved. The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.     

A fractional-order model for the novel coronavirus (COVID-19) outbreak

Nonlinear Dynamics - The outbreak of the novel coronavirus (COVID-19), which was firstly reported in China, has affected many countries worldwide. To understand and predict the transmission...     

Nonlinear incidence rate of a pest management SI model with biological and chemical control concern

A pest management SI model with impulsive releases of infective pests and spraying pesticides is proposed and investigated. We prove that all solutions of the model are uniformly ultimately bounded. We also obtain the sufficient conditions of globally asymptotic stability periodic solution of pest-extinction and permanence of the model. The approach of combining impulsive releasing infective pests with impulsive spraying pesticides provides reliable tactical basis for the practical pest management.     

Control of a multigroup COVID-19 model with immunity: treatment and test elimination

Nonlinear Dynamics - This paper introduces a multigroup COVID-19 model with immunity, in which the total population of each group is partitioned into five compartments, that is, susceptible,...